Benefits of Algorithmic Trading

Algo-trading provides the following benefits:

• • Trades are executed at the best possible prices.
  • Trade order placement is instant and accurate (there is a high chance of execution at the desired levels).
  • Trades are timed correctly and instantly to avoid significant price changes.
  • Reduced transaction costs.
  • Simultaneous automated checks on multiple market conditions.
  • Reduced risk of manual errors when placing trades.
  • Algo-trading can be backtested using available historical and real-time data to see if it is a viable trading strategy.
  • Reduced the possibility of mistakes by human traders based on emotional and psychological factors.

Most algo-trading today is high-frequency trading (HFT), which attempts to capitalize on placing a large number of orders at rapid speeds across multiple markets and multiple decision parameters based on preprogrammed instructions.

Algo-trading is used in many forms of trading and investment activities including:

• • Mid- to long-term investors or buy-side firms—pension funds, mutual funds, insurance companies—use algo-trading to purchase stocks in large quantities when they do not want to influence stock prices with discrete, large-volume investments.
  • Short-term traders and sell-side participants—market makers (such as brokerage houses), speculators, and arbitrageurs—benefit from automated trade execution; in addition, algo-trading aids in creating sufficient liquidity for sellers in the market.
  • Systematic traders—trend followers, hedge funds, or pairs traders (a market-neutral trading strategy that matches a long position with a short position in a pair of highly correlated instruments such as two stocks, exchange-traded funds (ETFs), or currencies)—find it much more efficient to program their trading rules and let the program trade automatically.

Algorithmic trading provides a more systematic approach to active trading than methods based on trader intuition or instinct.

Algorithmic trading

Algorithmic trading allows traders to perform high-frequency trades. The speed of high-frequency trades used to measure to milliseconds. Today, they may be measured in microseconds or nanoseconds (billionths of a second)

Implementation

Implementing the algorithm using a computer program is the final component of algorithmic trading, accompanied by backtesting (trying out the algorithm on historical periods of past stock-market performance to see if using it would have been profitable). The challenge is to transform the identified strategy into an integrated computerized process that has access to a trading account for placing orders. The following are the requirements for algorithmic trading:

• • Computer-programming knowledge to program the required trading strategy, hired programmers, or pre-made trading software.
  • Network connectivity and access to trading platforms to place orders.
  • Access to market data feeds that will be monitored by the algorithm for opportunities to place orders.
  • The ability and infrastructure to backtest the system once it is built before it goes live on real markets.
  • Available historical data for backtesting depending on the complexity of rules implemented in the algorithm.

The Securities and Exchange Commission noted in a 2018 report that electronic and algorithmic trading have become key pillars supporting today’s capital market operations.